Skin cancer is an increasing health problem globally with over one million new cases diagnosed each year in the United States alone, including almost 60,000 new cases of melanoma, the most serious form of skin cancer, and more than 8,000 deaths. Despite significant fundamental and clinical research efforts, the treatment of advanced melanoma has only shown minimal impact on the overall prognosis for this disease. The focus on skin cancer treatment traditionally has been on improved treatments for final stages and prevention. The statistics indicate that most resources are expended in the later stages of skin cancer where the probability is lower for a full recovery. It may be beneficial to the public and the health care insurance industries to shift resources to early skin cancer detection where probabilities increase significantly for survival and a continued productive life.
One difficulty with early skin cancer detection is that there is no objective method for skin cancer screening available for use in a clinical setting. Conventionally, skin cancer screening is performed by combining visual observations with manual handwritten tracking methods done locally in a physician's office. Digital photography has been used by some dermatologists and patients to help identify skin changes, but it remains difficult to compare baseline images to lesions observed at the time of a skin examination. One of the more important melanoma risk factors are persistently changing moles in size, and color, and the presence of a large number of moles of at least a certain diameter. The difficulty in imaging the human body over time, aligning features of the images, and comparing those images in a reliable, and clinically useful way is not currently available.
Thus, there are general needs for systems and methods for precisely aligning skin features in images captured over time and detecting changes in the skin features that may be suitable for use in early skin cancer detection.